Many devices and systems allow a scene to be captured by generating image and/or video data of the scene. For example, a camera can be used to capture images of a scene for recreational use, for surveillance, and/or for other applications. The image data from image capture devices and systems can be captured and output for processing and/or consumption.
Object recognition can be used to identify or verify an object from a digital image or a video frame of a video clip. One example of object recognition is face recognition, where a face of a person is detected and recognized. In some cases, the features of a face are extracted from an image and compared with features stored in a database in an attempt to recognize the face. In some cases, the extracted features are fed to a classifier and the classifier will give the identity of the input features.
In some cases, neural networks can be used to perform object detection and recognition, among other tasks. Given large amounts of data maintained by neural network based systems, such systems can general high quality object detection and recognition results. While neural networks (e.g., deep learning networks) have proven to be very versatile and accurate in a variety of tasks, such networks require high memory bandwidth and high computation cost.